MERL is looking for a highly motivated and qualified intern to work on developing algorithms for robot learning from videos of human demonstrations. The ideal candidate would be a current Ph.D. student with a strong background in computer vision, deep learning, and robotics. Familiarity with imitation learning, learning from demonstrations (LfD), reinforcement learning, and machine learning for robotics will be valued. Proficiency in Python programming is necessary and experience in working with a physics engine simulator like Mujoco or pyBullet is a plus. A successful candidate will collaborate with MERL researchers and publication of the relevant results is expected. Start date is flexible and the expected duration of the internship is 3-4 months. Interested candidates are encouraged to apply with their recent CV and list of publications in related topics. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

We are seeking a highly motivated intern to conduct original research extending MERL's work on uncertainty estimation in face landmark localization (the LUVLi model) to the domains of 3D faces and video sequences. The successful candidate will collaborate with MERL researchers to design and implement new models, conduct experiments, and prepare results for publication. The candidate should be a PhD student in computer vision and machine learning with a strong publication record. Experience in deep learning-based face landmark estimation, video tracking, and 3D face modeling is preferred. Strong programming skills, experience developing and implementing new models in deep learning platforms such as PyTorch, and broad knowledge of machine learning and deep learning methods are expected.

MERL is looking for a self-motivated intern to work on the problem of video anomaly detection. The intern will help to develop new ideas for improving the state of the art in detecting anomalous activity in videos. The ideal candidate would be a Ph.D. student with a strong background in machine learning and computer vision and some experience with video anomaly detection in particular. Proficiency in Python programming and Pytorch/Tensorflow is necessary. You are expected to collaborate with MERL researchers to develop algorithms and prepare manuscripts for scientific publications. The internship is for 3 months and the start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.